Training Workshop: "Biomedical Signal Processing Using MATLAB – From Preprocessing to Classification"
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2025-04-06
Training Workshop: "Biomedical Signal Processing Using MATLAB – From Preprocessing to Classification"

As part of its ongoing efforts to enhance technical and research skills in the field of biomedical signal processing, the Biomedical Engineering Research Center at the University of Anbar, in collaboration with the Center for Continuing Education, organized a specialized training workshop entitled: "MATLAB for EEG Signal Data Analysis – From Preprocessing to Classification", held on April 6, 2025, and delivered by Dr. Eng. Ali Amer Ahmed, an expert in intelligent control systems and biomedical signal processing. The workshop aimed to equip participants with advanced knowledge that combines both theoretical foundations and practical applications in the analysis of biomedical signals using the MATLAB platform, with a particular focus on electroencephalographic (EEG) and electromyographic (EMG) signals. The event saw active participation from researchers and staff members of the Biomedical Engineering Research Center, as well as specialists in the fields of biomedical engineering, electrical engineering, and computer engineering. Participants demonstrated high levels of engagement with the lectures and hands-on sessions, reflecting the significance of such training programs in strengthening research and applied capacities in the field of biomedical engineering. The main topics covered in the workshop included:(Fundamentals of biomedical signal analysis, focusing on EEG and EMG signals ,Techniques for noise reduction and signal enhancement, contributing to higher data quality ,Time-domain and frequency-domain analysis using MATLAB tools, enabling a deeper understanding of patterns in biomedical signals , Practical exercises using real EEG data, to enhance participants' applied skills , An introduction to integrating the MATLAB platform with artificial intelligence techniques, aimed at accurately classifying biomedical signals and opening new horizons for intelligent medical applications)